Papers Spatio-Temporal Forecasting
“Spatio-Temporal Forecasting” 태그가 달린 논문 87편 · 필터 해제
Beyond Attention: Learning Spatio-Temporal Dynamics with Emergent Interpretable Topologies
Spatio-temporal forecasting is critical in applications such as traffic prediction, energy demand modeling, and weather monitoring. While Graph Attention Networks (GATs) are popular for modeling spatial dependencies, the…
Computational EfficiencyGraph AttentionSpatio-Temporal ForecastingTraffic PredictionLearning with Calibration: Exploring Test-Time Computing of Spatio-Temporal Forecasting
Spatio-temporal forecasting is crucial in many domains, such as transportation, meteorology, and energy. However, real-world scenarios frequently present challenges such as signal anomalies, noise, and distributional shi…
Spatio-Temporal ForecastingDRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting
Accurate predictions of spatio-temporal systems' states are crucial for tasks such as system management, control, and crisis prevention. However, the inherent time variance of spatio-temporal systems poses challenges to …
RelationSpatio-Temporal ForecastingBilinear Spatiotemporal Fusion Network: An efficient approach for traffic flow prediction
Accurate traffic flow forecasting is critical for intelligent transportation systems, yet increasing model complexity in spatiotemporal graph neural networks does not always yield proportional gains. In this paper, we pr…
Spatio-Temporal ForecastingTime SeriesTraffic PredictionZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish
Data-driven benchmarks have led to significant progress in key scientific modeling domains including weather and structural biology. Here, we introduce the Zebrafish Activity Prediction Benchmark (ZAPBench) to measure pr…
Activity PredictionMultivariate Time Series ForecastingSpatio-Temporal ForecastingTime Series Forecasting+1Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning
Multivariate Time Series Forecasting (MTSF) has long been a key research focus. Traditionally, these studies assume a fixed number of variables, but in real-world applications, Cyber-Physical Systems often expand as new …
Contrastive LearningMultivariate Time Series ForecastingSpatio-Temporal ForecastingTime Series+1Temporal Graph MLP Mixer for Spatio-Temporal Forecasting
Spatiotemporal forecasting is critical in applications such as traffic prediction, climate modeling, and environmental monitoring. However, the prevalence of missing data in real-world sensor networks significantly compl…
Spatio-Temporal ForecastingTraffic PredictionSpatio-Temporal Forecasting of PM2.5 via Spatial-Diffusion guided Encoder-Decoder Architecture
In many problem settings that require spatio-temporal forecasting, the values in the time-series not only exhibit spatio-temporal correlations but are also influenced by spatial diffusion across locations. One such examp…
DecoderGraph Neural NetworkSpatio-Temporal ForecastingTime SeriesInfinite-dimensional next-generation reservoir computing
Next-generation reservoir computing (NG-RC) has attracted much attention due to its excellent performance in spatio-temporal forecasting of complex systems and its ease of implementation. This paper shows that NG-RC can …
Spatio-Temporal ForecastingCausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions toward Public Events
Large-scale human mobility exhibits spatial and temporal patterns that can assist policymakers in decision making. Although traditional prediction models attempt to capture these patterns, they often interfered by non-pe…
ArticlesCausal InferencePredictionSpatio-Temporal Forecasting+1Distribution-aware Online Continual Learning for Urban Spatio-Temporal Forecasting
Urban spatio-temporal (ST) forecasting is crucial for various urban applications such as intelligent scheduling and trip planning. Previous studies focus on modeling ST correlations among urban locations in offline setti…
Continual LearningSchedulingSpatio-Temporal ForecastingGEPS: Boosting Generalization in Parametric PDE Neural Solvers through Adaptive Conditioning
Solving parametric partial differential equations (PDEs) presents significant challenges for data-driven methods due to the sensitivity of spatio-temporal dynamics to variations in PDE parameters. Machine learning approa…
Spatio-Temporal ForecastingSparse Decomposition of Graph Neural Networks
Graph Neural Networks (GNN) exhibit superior performance in graph representation learning, but their inference cost can be high, due to an aggregation operation that can require a memory fetch for a very large number of …
Graph Neural NetworkGraph Representation LearningNode ClassificationRepresentation Learning+1Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting
The widespread deployment of sensing devices leads to a surge in data for spatio-temporal forecasting applications such as traffic flow, air quality, and wind energy. Although spatio-temporal graph neural networks have a…
Graph Neural NetworkSpatio-Temporal ForecastingAutoSTF: Decoupled Neural Architecture Search for Cost-Effective Automated Spatio-Temporal Forecasting
Spatio-temporal forecasting is a critical component of various smart city applications, such as transportation optimization, energy management, and socio-economic analysis. Recently, several automated spatio-temporal for…
energy managementModel OptimizationNeural Architecture SearchSpatio-Temporal ForecastingReprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning
Spatio-temporal forecasting plays a crucial role in various sectors such as transportation systems, logistics, and supply chain management. However, existing methods are limited by their ability to handle large, complex …
Language ModelingLanguage ModellingRepresentation LearningSpatio-Temporal Forecasting+1Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings
Spatio-temporal forecasting is crucial in transportation, logistics, and supply chain management. However, current methods struggle with large, complex datasets. We propose a dynamic, multi-modal approach that integrates…
Decision MakingMixture-of-Expertsparameter-efficient fine-tuningSpatio-Temporal Forecasting+2Language Model Empowered Spatio-Temporal Forecasting via Physics-Aware Reprogramming
Spatio-temporal forecasting is pivotal in numerous real-world applications, including transportation planning, energy management, and climate monitoring. In this work, we aim to harness the reasoning and generalization a…
energy managementLanguage ModelingLanguage ModellingSpatio-Temporal Forecasting+2Geolocation Representation from Large Language Models are Generic Enhancers for Spatio-Temporal Learning
In the geospatial domain, universal representation models are significantly less prevalent than their extensive use in natural language processing and computer vision. This discrepancy arises primarily from the high cost…
Spatio-Temporal ForecastingTime Series ForecastingREGIONAL DATASETS FOR AIR QUALITY MONITORING IN EUROPEAN CITIES
The primary environmental health threat in the WHO European Region is air pollution, impacting the daily health and well-being of its citizens significantly. To effectively understand the impact, and dynamics of air qual…
Multivariate Time Series ImputationSpatio-Temporal ForecastingTime Series